The determination of the most energy-efficient path between two locations, which may be referred to as endpoints, in a network (where the endpoints may be image locations, network locations, geographic locations, or other types of locations) in which there are multiple possible paths between the two endpoints can be a very complex problem.
A simple approach to addressing the problem may be to choose the most efficient path at each juncture along the path, which may be referred to as a “local” optimization effort, but such a local optimization effort can lead to sub-optimal paths when compared to other possibilities. For this reason, such problems generally need to be considered “globally”, considering all the possible paths available between the two endpoints.
However, conventional processing to determine such a global solution requires a sequence of many calculations in multiple iterations, and thus poses a significant overhead to generate a solution to the energy-efficient path problem.